The present invention relates to defect detection systems and particularly to systems for detecting defects in manufactured parts and other components that may fail under mechanical stress due to some inherent structural defect or anomaly. More particularly, the present invention relates to defect detection systems and methods for detecting defects in manufactured parts, which systems and methods employ a knowledge database storing image processing and computational algorithms and inspection criteria, all of which are part dependent.
Certain manufactured parts are subject to mechanical stresses under operating conditions which can cause a defective part to fail without warning, with often dangerous consequences. Defects in these manufactured parts, fabricated by layup, casting, lost wax, mold injection, superplastic forming processes and other types of processes, may be detectable as anomalies in the surface or in the structure of the finished manufactured part. Surface defects include cracks, pits and excessive porosity. Structural defects include unbonds and delaminations.
For quality control and assurance, manufactured parts are subject to part inspections having predefined acceptance criteria applied against suspect anomalies. Typically, an inspector visually inspects the manufactured part for certain anomalies, and if the visually found anomalies are outside the bounds of the acceptance criteria, then the anomalies are considered defects and the part is rejected.
In modern fabrication facilities, more and more parts are fabricated using automated tools and machines with increased through-put and quality. As more automation is applied to the fabrication and assembly of parts, manual inspection accounts for an increasing percentage of the total manufacturing costs; thus, limiting cost reductions afforded by modern automation. Also, inspectors, unlike machines, are subject to fatigue and human error. The cost of inspection and nondestructive evaluation of parts in industry is becoming a larger percentage of the total cost of many component parts and assemblies. For these reasons, it is desirable to automate the inspection process whenever economically feasible.
Certain manufactured parts have been subject to nondestructive visual inspection procedures to aid inspectors in their task.
At present, both radiographic and ultrasonic imaging inspection techniques are being used in the aerospace industry to inspect composite graphite assemblies or parts. Composite assemblies are subject to defects such as delaminations between graphite surface. The part under inspection is generally held rigid while transmit transducers and receive sensors are raster-scanned line-by-line across the part. The received energy may be formatted and processed to form a digital image represented by intensity-modulated pixels (picture elements) on a display screen.
Radiographic and ultrasonic testing are, in many ways, complementary, and hence are used in concert to interpret the condition of a part to a higher degree than can be achieved with each test separately. A greater magnitude of received radiographic energy through a particular region of a part indicates a low density region of the part. On the other hand, a large amount of sensed ultrasonic energy through the part generally corresponds to a relatively higher density region. Radiographic energy will resolve finer detail, but is unable to detect certain defects such as unbonding that is readily detected by ultrasonic testing.
Testing methods to date, even automatic methods, have not addressed the need for combining processing and analysis of complementary inspection methods on a part. Inspection and defect interpretation procedures vary between different nondestructive inspection methods, yet there remains much commonality between methods that can lead to utilization of combined inspection knowledge bases.
U.S. Pat. No. 4,484,081, Cornyn et al., discloses a method for automatic inspection of parts using particular image processing techniques whereby a digitized video frame of a part under analysis is subjected to a thresholding means that utilizes a histogram computation to determine an optimum, threshold level to produce a binary anomaly image, said image being further processed by a region growing means to define and label unique anomalies in the image.
The anomalies are then measured with respect to their size. Decisions are then made as to whether each such anomaly constitutes a rejectable defect based upon predetermined rejection inspection criteria. In so doing, the shape, size and orientation of the part are used to compute region statistics such as area, and width-to-length ratio. These region statistics are then compared to an acceptance criteria table, that is, a region analysis means, relative to the region statistics to determine if the anomaly is a rejectable defect.
Hence, Cornyn et al '081 is directed to a particular automated defect detection system using a particular image processing technique whereby anomalies are detected based upon a particular thresholding means and labeled based on a region growing means. Furthermore, Cornyn et al is directed to a particular rejection criteria table based upon the calculated parameters and statistics which are the area, width-to-length ratio, and proximity to other defects. The system therein disclosed may be well suited for automated inspection of a single particular part.
However, both assembled and component parts are manufactured in an infinite variety of shapes and assembled structures using a wide variety of different materials. Each particular assembly or component part may be better and more completely tested using several different types of nondestructive imaging tests which depict the part under test. Using different image processing techniques to process different digitized images and using a wide variety of different inspection criteria, the part may be further tested. There is, therefore, a need for a new defect recognition system and method.